DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 17 February 2026, has been entered.
Although claim 16, in the previous Office Action was thought to be of allowable subject matter, however, in the process of an updated search certain prior art are thought to reasonably address such claim limitations.
Nonetheless, Examiner looks forward to receive further amendments/Interviews and to expedite allowance of this application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 16 is rejected under 35 U.S.C. 103(a) as being unpatentable over Chen et al. (USPAP 2011/0035,379), hereinafter, “Chen” in view of Yanagida (JP H11-39142 A), and further in view of Brothers et al. (USPAP 2018/0082,181), hereinafter, “Brothers”.
Regarding claim 16, Chen recites, augmentation in a classifier (Please note, paragraph 0021. As indicated an evaluation of latent product clustering and recommendation ranking models, using real-world e-commerce data from eBay, in both offline simulation and online testing (e.g., alpha testing). In recent testing, a test system yielded significant improvements over an existing production system with respect to click-through counts, purchase-through counts, and gross merchandising value. An example embodiment may provide a practical yet principled framework for recommendation of items in domains with affluent user self-input data.) comprising: for each location on the feature map: performing a Bernoulli sampling under a sampling probability; for each location chosen by the Bernoulli sampling (Please note, paragraph 0035. As indicated the one-trial specialization may be appropriate for the item data. Sellers tend to use concise descriptions for listing items, which may support an assumption that repeated terms are less relevant. Values of a categorical attribute tend to be mutually exclusive, which may support an assumption that an item has exactly one value. Another assumption used in various example embodiments is to assume the variance .sigma..sup.2 of a continuous variable is a constant with respect to a collection of items. A latent product is thus represented as a vector of Bernoulli success probabilities, multinomial parameters, and Gaussian means); generating a first unit having a centroid at the location. (Please note, paragraph 0051. As indicated example embodiments use machine-learning to learn a relatively large number of latent products with high intra-product uniformity to make precise recommendations, while keeping the size of each product cluster above a certain threshold to maintain an acceptable recall level. Hence, the following heuristics for cluster management may apply: 1. Given n input item examples, initialize m=.left brkt-bot.n/d.right brkt-bot. centroids as generating latent products by random sampling (e.g., as described above with respect to Parameter Estimation). Here, d is the target average size of a cluster (e.g., d=20)).
Chen does not expressly recite, randomly generating a shifting range; randomly generating one or more additional units having unit centroids within the shifting range.
Yanagida recites, randomly generating a shifting range; randomly generating one or more additional units having unit centroids within the shifting range. (Please note, page 3, third paragraph. As indicated with reference to FIG. In FIG. 5, using the first Bernoulli map and the second tent map, which are the chaotic maps described above, to generate a random number sequence and generate a random number, the following configuration is used. First, an appropriate initial value x n, substituted Bernoulli map 11 consisting of a random sequence generator for generating a random number sequence using a chaotic map, the equation (2), x n with (3) Calculate +1 . If the value of x n + 1 is the interval [0,1 / 2], "0"; If the interval is [1/2, 1], the sequence is set to "1", and the 0/1 conversion unit 12 generates a sequence of "0" and "1". The set of e number sequences generated first is stored in the shift register 1 in the order of generation. Input the data in 3 from left to right. Since the sequence input to the shift register 13 is a binary number, the binary number is 10. The number is converted into a decimal number by the base number conversion unit 14, and the first random number is obtained.).
Chen & Yanagida are combinable because they are from the same field of endeavor.
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to utilize this randomly generating a shifting range; randomly generating one or more additional units having unit centroids within the shifting range of Yanagida in Chen’s invention.
The suggestion/motivation for doing so would have been as indicated on page 3, 4th. Paragraph, “Since the maximum value of the random numbers obtained in this manner is a non-negative integer of 2 e , (mod m) is used to obtain a random number of any positive integer equal to or less than m. As the number e for extracting the sequence, for example, the number of bits of one word of a computer to be used is selected.”.
Chen and Yanagida do not expressly recite, swapping values among the two or more units.
Brothers recites swapping values among the two or more units. (Please note, paragraph 0042. As indicated feature map 2 and feature map 3 are swapped by redefining the neural network and there is also swapping of the corresponding weight kernels.).
Chen, Yanagida & Brothers are combinable because they are from the same field of endeavor.
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to utilize swapping values among the two or more units of Brothers in Chen & Yanagida’s invention.
The suggestion/motivation for doing so would have been as indicated on paragraph 0042, “reordering shuffles the IFMs in the network to get an equivalent network that is more load balanced.”.
Therefore, it would have been obvious to combine Chen, Yanagida with Brothers to obtain the invention as specified in claim 16.
Examiner’s Note
The examiner cites particular figures, paragraphs, columns and line numbers in the references as applied to the claims for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages and figures may apply as well.
It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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/AMIR ALAVI/Primary Examiner, Art Unit 2668 Friday, March 6, 2026